To address the problem of high lifespan loss and poor state of charge (SOC) balance of electric vehicles (EVs) participating in grid peak shaving

an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving scenarios.First

considering the difference between peak and valley loads and the operating costs of EVs

a peak shaving model for EVs is constructed.Second

the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer (GEO) algorithm.Subsequently

IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next

using the k-means algorithm

EVs are dynamically divided into priority charging groups

backup groups

and priority discharging groups based on SOC differences.Finally

a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs

whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified

and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The proposed EV grouping control strategy effectively reduces the peak-valley difference in the power grid

reduces the operational life loss of EVs

and maintains a better SOC balance for EVs.